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The video explains how to install PyTorch using the Anaconda Python package manager and recommends using version 9.0 of CUDA. Users can verify whether PyTorch is installed by running the command Conda list PyTorch. The video also introduces the software tools that will be used in the series and presents the directory structure for the project. It is recommended to import the top-level PyTorch package before running any commands. Additionally, the video explains that using a GPU is not necessary to use PyTorch but is recommended for larger projects, with the importance of GPUs emphasized in writing immense software.
In this section, the process of installing PyTorch is discussed, with the recommended option being to use the Anaconda Python package manager. After installing Anaconda, users can go to the PyTorch website to find the appropriate command to run on their system. The recommended version of CUDA is 9.0, as there have been some issues with version 9.2. Once PyTorch is installed, users can verify by running the command Conda list PyTorch. Additionally, the software tools that will be used throughout the series, including Visual Studio Code and Jupyter Notebook, are introduced. The directory structure for the project is also presented. Finally, users are encouraged to import the top-level PyTorch package before running any commands.
In this section, the video demonstrates how to check the version of PyTorch and CUDA installed on the system using Torch. The video also mentions that to use PyTorch, having a GPU is not necessary and the series can be followed without it, but it's recommended to use it when attacking larger projects. It's explained that NVIDIA GPU should support CUDA, which can be checked on Nvidia's website. Finally, the importance of GPUs in writing enormous software is emphasized.
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